ES2776178T3 - Systems to detect brain-based bio-signals - Google Patents

Abstract

A system for detecting a brain-based multi-component electromagnetic bio-signal (100) comprising: at least two biosignal sensors (11, 12, 13, 52, 60, 70) that are part of an oral fixation device (800 , 900, 1000) configured to be placed in an oral cavity to bring the at least two bio-signal sensors into contact with a buccal tissue (10), wherein said at least two bio-signal sensors have a cable connection or wireless with at least one external computer processing unit (650), the at least two bio-signal sensors being configured to detect a raw multicomponent oral electromagnetic bio-signal in oral tissue and send the oral electromagnetic bio-signal raw multicomponent to the at least one external computer processing unit (650) that includes a separate component analysis processor (700) that isolates at least one raw multicomponent electromagnetic bio-signal brain-based maria (100) of a plurality of other subcomponents (710, 720, 730) within the raw multicomponent oral electromagnetic biosignal, and wherein said external computer processing unit (650) further includes a plurality of brainwave bandpass filters (750) that are configured to further separate the brain-based raw multicomponent signal (100) into a plurality of frequency brainwave bands (751, 752).

Description

DESCRIPTION

Systems for detecting brain-based bio-signals

Cross Reference to Related Requests

This application claims the priority benefit of US Provisional Application No. 61 / 717,907 filed on October 24, 2012, and US Provisional Application No. 61 / 790,007.

Field of the invention

The present invention relates to a system for detecting a brain-based multicomponent electromagnetic biosignal. Also described is a method for detecting, processing and extracting a variety of partial signals from a multicomponent brain-based signal detected in the oral cavity.

Background of the invention

The use of electroencephalogram ("EEG") sensors located on the scalp to detect brain activity has been known in the art since the 1920s. As the electric field generated by brain activity is very small, the EEG alone you can recognize it if large sets of neurons show similar behavior. The resulting neural EEG signals are in the microvolt (pV) range and can be easily masked by interference from man-made sources causing artifacts in the signal. Typically, artifacts in an EEG signal are caused by the subject's non-neuronal physiological activities or by external technical sources. Non-neural physiological activities can include blinking, eye movements, muscle activity in the vicinity of the head (eg, facial muscles, jaws, tongue, neck), heartbeat, pulse and Mayer waves, and the like. External technical sources may include cables with oscillation in the earth's magnetic field, improper grounding, power supplies or transformers, radio waves, and the like.

Other ways to monitor the electrical activity of the brain are based on invasive procedures that include needle electrodes (sharp wires placed between the scalp and the skull); cortical electrodes, subdural electrodes and depth electrodes. The characteristics of the electrical activity of the brain monitored with invasive electrodes are related to surface electrodes such as EEG electrodes on the scalp or skin, but they are different since attenuation and propagation of the signal through the scalp is avoided and the skin. Document WO 2012/027648 describes an intraoral multisensor device that includes a mouthpiece, a plurality of sensors, for example EEG sensors, at least one of the sensors connected or integrated with the mouthpiece, and a data communications unit configured to receive signals from the plurality of sensors. The mouthpiece has a shape that allows for a stable arrangement, at least partially, within a person's mouth, so that it can remain for hands-free detection of a plurality of biological parameters.

Therefore, it is desirable to detect and monitor brain activity and brain-based biological signals with little interference from other electrical sources and without requiring invasive procedures.

The description herein of certain advantages and disadvantages of known methods and devices is not intended to limit the scope of the present invention. In fact, the present embodiments may include some or all of the features described above without suffering from the same disadvantages.

Summary of the invention

The present invention provides a system for detecting a brain-based multi-component electromagnetic biosignal as defined in claim 1.

In view of the foregoing, it is a feature of the embodiments described herein to provide a system for monitoring electromagnetic activity in the brain through the oral cavity without using invasive procedures. Brain-based electromagnetic biological signals in particular can include multi-component signals that form a multi-component brain-based signal, which can include signals that are generated from other parts of the body, including the central nervous system, the electrical activity of the heart , lung activity (respiration), local arterial movement, ocular dipole electrical activity (and other dipoles), muscle electrical activity, and local tissue electrical activity, such as that generated by the peripheral nervous system, as well as brain-based electromagnetic signals. The multicomponent brain-based signal is detected by sensors placed in the oral cavity. The brain-based multicomponent signal can then be digitized, amplified, and filtered. After filtering the desired subcomponent biological signals they are isolated from the brain-based multicomponent signal for further analysis.

For the purposes of this invention, the term "brain-based multicomponent signal" is used to describe this collection of subcomponent biosigns, since the biosignals of interest from the primary component emanate from the brain. The brain-based multicomponent signal can include bio-electromagnetic signals, cardiac electromagnetic bio-signals, local tissue bio-electromagnetic signals; bioelectric bio-signals of the ocular dipole; bio-electromagnetic muscle bio-signals; bio-electromagnetic signals from the tongue; bio-signals related to heart rate; pulsatile bio-signals related to respiration; bio-signals related to movement; biomechanical bio-signals; bioacoustic bio-signals. The component signals of the brain-based multicomponent signal are important for many applications (eg, medical, veterinary, and non-medical applications). Because the brain-based signal detectors of this invention are located in the oral cavity, the detectors can detect electrical activity from many parts of the brain including the cerebral cortex, as well as other parts of the brain.

Brief description of the drawings

Figure 1 is a comparative schematic view of multi-component hard palate brain based bio-signal detection versus scalp EEG brain wave detection.

Figure 2 is a schematic view of the brain-based multicomponent biosignal, hard palate and the resulting subcomponent waves after extraction.

Figure 3 is a comparative schematic view of a hard palate alpha wave subcomponent signal extracted versus scalp EEG alpha waves.

Figure 4 is a schematic overlay view of a bio-signal subcomponent signal passed in the brain from the hard palate extracted against EEG brain waves from the scalp during mental counting activity.

Figure 5 is a comparative schematic view of an extracted hard palate bio-signal subcomponent signal based on brain versus raw EOG brain waves during up-down eye movement.

Figure 6 is a comparative schematic view of a brain-based hard palate bio-signal subcomponent signal extracted versus raw EOG brain waves during left-right eye movement.

Figure 7 is a comparative schematic view of a brain-based hard palate bio-signal subcomponent signal extracted versus cardiac ECG waves.

Figure 8 is a comparative schematic view of a brain-based hard palate bio-signal subcomponent signal extracted versus EEG, EOG, and respiration waves.

Figure 9 are schematic views of various embodiments of the electrodes of the invention.

Figure 10 is a schematic view of a system for detecting sleep disorders including internal and external units.

Figure 11 is a schematic view of an alternative embodiment of the multiple sensor system.

Figure 12 is an isometric view of one embodiment of an oral fixation device.

Figure 13 is an isometric view of one embodiment of an oral fixation device.

Figure 14 is an isometric view of one embodiment of an oral fixation device.

Figure 15 is a sagittal view of a human head with the oral fixation device of Figure 12 inserted into the oral cavity.

Detailed description of the invention

The following description is intended to convey a complete understanding of the embodiments by providing a number of specific embodiments and details involving methods for detecting and processing brain-based multi-component signals in an oral cavity. However, it is understood that the invention is not limited to these specific embodiments and details, which are by way of example only. It is further understood that one of ordinary skill in the art, in light of known devices, systems and methods, would appreciate the use of the invention for the intended purposes and benefits in any number of alternative embodiments.

In a preferred embodiment of the invention, a system for detecting brain-based multi-component electromagnetic biosignals includes a sensor in the oral cavity that may be coupled to one or more electronic processors capable of electronically digitizing, amplifying, attenuating. filter and normalize brain-based multi-component biological signals as needed. The computer processor may also be capable of extracting, isolating, or otherwise dividing subcomponent signals from brain-based multi-component biosignals, and optionally classifying and analyzing subcomponent signals.

The sensor can be an electrical or magnetic sensor capable of detecting brain-based multi-component electromagnetic bio-signals. In a preferred embodiment, the sensor includes electrodes that are in contact with the hard palate, where one electrode acts as a reference for comparison with one or more electrodes.

The electrodes can be resistive mode electrodes, capacitive mode electrodes, current mode electrodes, or inductive mode electrodes. The electrodes can be passive electrodes that simply receive a signal, or they can be active electrodes that can digitize or process the received signal with an internal electronic processor.

In some embodiments, the sensor in the oral cavity may be included in an oral device configured to engage the dentition or other oral tissue. In additional embodiments, the position of the sensor or sensor electrodes may be adjustable relative to the oral device.

In some embodiments, the sensor may be communicatively coupled with a processor in the oral device. In other embodiments, the sensor may be communicatively coupled to one or more external processing devices. The sensor and / or processor (s) may be communicatively coupled via cables and / or wirelessly, such as Bluetooth or other wireless technology.

Figure 1 is a comparison of brain-based multi-component hard palate 10 bio-signal versus a standard EEG brain wave signal from the scalp. A sensor that includes a reference electrode 11 and a left signal electrode 12 coupled to or proximate to the hard palate 10 is used to detect the multicomponent brain-based biological signal 100. In some embodiments, the sensor may also include a signal electrode. right 13. The brain-based multicomponent bio-signal 100 is the raw hard palate bio-potential signal. The raw unfiltered scalp EEG signal 200 is detected from the F4A2 electrode 20 (F = frontal, 4 = right side of the scalp according to 10-20 standards) with a reference electrode on the right mastoid. The brain-based multi-component biosignal 100 is a relatively unremarkable pattern of the raw palate biopotential signal compared to the raw scalp EEG signal 200. The brain-based multi-component biosignal 100 has a range significantly higher voltage compared to 200 pV scalp EEG signal, 100 pV versus 10 pV respectively. This shows that special analysis of the brain 100-based multicomponent biosignal of the hard palate 10 is necessary to determine the subcomponents of the brain 100-based multicomponent biosignal of the hard palate 10 especially the subcomponent signals based on brain.

The reference electrode 11 was placed on the hard palate 10 to avoid mixing the scalp EEG with the brain-based oral signals. The left signal electrode 12 and the right signal electrode 13 can be gold or gold-plated electrodes covered with cotton gauze. Saline can be used in some embodiments to moisten the gauze.

Figure 2 shows the hard palate brain 100-based multi-component biosignal 10 being divided into its various subcomponent signals after being processed in accordance with embodiments of this invention. The subcomponent signals may include an 8-14 Hz brain wave subcomponent signal 101, an eye movement subcomponent signal 103, a cardiac subcomponent signal 104, and a respiration subcomponent signal 105.

Figure 3 shows the strong correlation between the 8-14 Hz brain wave subcomponent signal 102 detected in the hard palate and the 8-14 Hz brain wave EEG signal from the scalp 201.

Figure 4 shows the strong correlation between the 3.5 - 30 Hz brain wave subcomponent signal 102 detected in the hard palate and the 3.5 - 30 Hz brain wave scalp EEG signal 202 of a subject when the subject performed the mental activity of counting backward from 100 from 7 to 7 (ie, 100, 93, 86, 79, etc.). The subject was sitting in a well-lit, environmentally controlled room.

Figure 5 shows the strong correlation between an 80 pV 100 brain-based multicomponent bio-signal detected in the hard palate 10 and the 53 pV 300 brain wave EOG signal detected with EOG 30 electrodes on the human scalp of the side. right while the subject was looking quickly up and down. No filtering or isolation of subcomponents of the brain-based multicomponent biosignal was necessary for this embodiment.

Figure 6 shows the correlation between a 50 pV 100 brain-based multicomponent biosignal detected in the hard palate 10 and the 558 pV 300 brain wave EOG signal detected with EOG 30 electrodes on the right side human scalp. while the subject quickly looked left and right. No filtering or isolation of subcomponents of the brain-based multicomponent biosignal was necessary for this embodiment.

Figure 7 shows the correlation between the cardiac signals detected at 0.5 - 249 Hz in the brain wave subcomponent signal 104 in the hard palate 10 and the cardiac signals in an unfiltered ECG signal 400 detected with the electrode electrode. ECG 40. The subject was sitting in a well-lit, environmentally controlled room during recording. The brain-based multicomponent biosignal 100 was filtered in this application to eliminate DC drift.

Figure 8 shows the correlation between the respiratory subcomponent signals 106 at 1.5 Hz - 249 Hz extracted from the brain-based multicomponent bio-signals detected in the hard palate 10 and the EEG signals from the scalp 203, EOG signals from the right eye 301 were both filtered at 1.5 Hz - 249 Hz, and a nasal cannula breathing signal 500 while the subject breathed deeply and rapidly 501 and held the breath 502 for 20 seconds. The graph shows that the bio-potential of the hard palate changes at the same time that the EEG and EOG of the scalp change showing the strong temporal relationship between the multicomponent bio-signals of the hard palate and the signals related to the scalp.

Figure 9 shows various electrode embodiments for use in detecting multi-component hard palate biosignal to conform to the shape of oral tissue and provide comfort and biocompatibility. Soft materials (gauze or foam) can provide mechanical safety and hold electrolytes around the electrode. Other electrode materials can be used as desired. Electrode assembly 50 includes a metal electrode 51 and temperature sensor 52. The combination of electrode 51 with temperature sensing 52 for the oral cavity or other body locations. The electrodes detect the current flow of the tissue and the temperature sensor allows the determination of oral temperature, motion artifact (since temperature is not a bio-potential measure) and oral air flow. Average oral temperature can be estimated using a thermistor, semiconductor integrated circuit, thermocouple, or other appropriate sensor. The temperature variations arising from the air flow can be used to determine the presence / absence of air flow as part of the pre-processing or by the microcontroller (pCU). One embodiment of the electrode may be a convex electrode assembly 60 that may include a metal electrode with a conductive wire 61 and temperature sensor 62 and a soft, absorbent covering surface 63. Another embodiment of the electrode may be a concave electrode assembly 70. which may include a metal electrode with a conductive wire 71 and temperature sensor 72 and a soft absorbent covering surface 73. A third embodiment of the electrode may be a flat electrode assembly 80 which may include a metal electrode with a wire conductor 81 and a soft absorbent covering 82. Reference electrode 11 may be a circular metal electrode.

Figure 10 is a schematic of a system for detecting sleep disorders 600 that includes an internal oral unit 601 and an external unit 650. The internal oral unit 601 may include a convex electrode 60 positioned near the middle of the hard palate and one or two concave electrodes 70 placed on the left and / or right near the gums.

The internal oral unit 601 may include a sensor unit 602, a power supply 603, a power manager 604, a microcontroller 605, and a transmission unit 606. The internal oral unit 601 can amplify, filter and / or digitize bio -Multi-component signals using dedicated circuitry as shown or as part of the data management microcontroller (pCU) Digital signals can be passed to a radio frequency (RF) module for transmission to a remote receiver, for example a smartphone or computer, or to the cloud, etc.

Detection of brain-based multicomponent signals can be accomplished by placing the internal unit 601 within the appropriate body cavity, where detection of the brain-based multicomponent bio-signal can be initiated automatically (or manually). Signal detection generally begins immediately, however a temperature sensing component can be added to monitor ambient temperatures to ensure proper operating conditions and / or monitor temperature during data collection. The temperature sensor can also be used to monitor changes in air flow through the mouth. Additional sensors can also be added to monitor a variety of additional physiological variables, including oxygen saturation via a PPG optical sensor / monitor, accelerometer, gyroscope, GPS, pressure, camera, biological or chemical monitors, etc. Brain-based detectors monitor physiological signals from multiple parameters, including brain waves.

The detector (i.e., the sensor) can be based on any of the following sensors: resistance mode electrodes, capacitive mode electrodes, current mode electrodes, passive electrodes, active electrodes, magnetic mode detectors, magnetic mode detectors inductive, acoustic mode detectors, optical or electro-optical mode detectors, chemical or biochemical mode detectors, biological mode detectors, and brain-based detector arrays (the brain-based detector may also comprise multiple sensors oriented in different geometric planes ). The sensors can have various forms and include various metals, metal salts or metal alloys, semiconductors, polymers, carbon compounds, conductive fabrics, composites, graphenes, non-metals; the sensor comprises rigid, semi-rigid and other flexible materials. The sensors can use microelectronic technology. The sensors can be disposable and / or reusable. The sensors can include remote sensors. The sensors can be adjusted in position and / or performance to optimize brain-based multicomponent biosignal detection.

Sensor Unit 602 can detect electrical signals from the hard palate picked up by electrodes and can amplify and filter electrical signals to eliminate motion and other artifacts and transport them to the 605 microcontroller (pCU) via the SPI bus for further processing, storage and transmission .

Programming and management of signal and power are performed by the pCU 605. Energy consumed from a disposable or rechargeable power supply 603 can be minimized by the pCU 605 by controlling the duration and duty cycle of the devices. data collection, the 606 transmission module, and the 605 pCU itself. Intelligent power management can reduce the size and complexity of the source 603 power supply and eliminate the need for a power line operated system.

Transmission of data by transmission unit 606 may be through well-known standard communication protocols, such as Bluetooth (BT) and Bluetooth LE (Low Energy) (BLE), or proprietary protocols or frequencies. Using standard protocols can ensure easier post-transmission processing. The transmitter unit 606 can support both BT and BLE, which can be accessed by smartphones and other devices. An antenna of the transmitting unit. 606 can be built into the side and / or front walls of an oral appliance connection device as shown in Figures 12-15.

The external unit 650 includes a receiver unit 651, a preprocessing unit 652, an Independent Component Analysis ("ICA") processor 700, a brain-based multicomponent biosignal component analyzer 750.

Receiver unit 651 may be configured to receive signals from transmission unit 606. Preprocessing unit 652 removes as much signal noise as possible. The ICA 700 processor can use standard ICA algorithms to extract and isolate individual subcomponent signals to ensure a good estimate of the components of the brain-based raw hard palate signal, 1-N 751,752 and 753 brain wave filters, 710 eye movement signal processor, 720 heart signal processor and 730 respiration signal processor.

To ensure a good estimation of the sub-components of the brain-based raw multi-component hard palate biosignal, it is important to remove as much signal noise as possible with a 652 preprocessing unit. The 652 preprocessing unit can eliminate the non-physiological noise by filters and sensors (thermistors) integrated in electrodes or in a sensor platform. The oral fixation device can protect the electrodes on its posterior surface to avoid disturbance by the tongue or internal facial muscles. Thermistors also provide a means of detecting movement of the device relative to tissue, as well as a means of correcting for large changes in temperature due to respiration. Additional processing includes data filtering, such as low-pass filtering. Additional pre-processing may include centering and whitening. Centering removes the mean of each component by subtracting the mean of the data from the actual data. Data whitening is done so that the raw data is uncorrelated to ensure that each subcomponent is as independent as possible. Pre-processing can also identify eye movements due to the unique arrangement of the electrodes (left and right) that produce significant differences in the raw signal detected by each electrode. Root mean square values can be determined and threshold detectors can be incorporated.

Digitization by preprocessing unit 652 can be performed electronically to allow efficient digital processing, as well as signal amplification and / or attenuation of the bio-signals if necessary. Preprocessing also seeks to eliminate unwanted noise by algorithmically filtering, shielding, blocking or eliminating or removing unwanted physiological and non-physiological signals, such as electrical noise, acoustic noise, mechanical noise, other artifacts or galvanic currents of dissimilar metals, or tongue artifacts, etc. Undesirable artifacts contained in biological signals can make recordings difficult. Signal normalization can also occur at this stage.

The ICA processor 700 determines the individual subcomponent signals of the hard palate multicomponent biosignal without previously knowing each component. To effectively determine each sub-component, the number of detectors (sensors) must be equal to or greater than the number of individual signal components. Embodiments can use three electrodes to detect biological potentials, each with a built-in thermistor providing six (6) detectors in total. This embodiment can detect 4 subcomponent biosignals. To separate the components, the ICA processor may incorporate the JADE (Approximate Joint Diagonalization Matrices) algorithm, which tends to work best for small data sets.

700 from a computer or Field Programmable Gate Array (FPGA).

Extracting, isolating, or dividing the detected brain-based multicomponent signal into signals for individual subcomponents may involve appropriate means of extracting, isolating and / or dividing the brain-based multicomponent signal into constituent physiological signals and / or other signals, as required. want.

The primary subcomponent signals may include brain-based bio-electromagnetic signals, cardiac electromagnetic biosignals, ECG, local tissue bio-electromagnetic signals; ocular dipole bioelectric biosignals, muscle bio-electromagnetic biosignals, tongue bio-electromagnetic biosignals, cardiovascular related pulsatile biosignals (eg, blood volume pulse); pulsatile biosignals related to respiration, biosignals related to movement, biomechanical biosignals and / or bioacoustic biosignals. Each sub-component signal generally includes multiple frequencies and can have different dynamic ranges that can overlap. In some embodiments, additional physiological parameters can be obtained, including heart rate, respiratory rate, heart rate variability, pulse transit time, blood pressure.

A variety of signal processing or signal analysis means (implemented in algorithms) to extract the bio-signals of the described subcomponents. Subcomponent biosignal extraction may include the use of pattern recognition, independent component analysis, principal component analysis, linear analysis, frequency domain analysis, time-frequency and nonlinear techniques such as the correlation dimension ( CD), phase space graphs, different entropies, based on wavelets, Hilbert-Huang Transforms (HHT), and the like as desired.

For some applications, signal isolation or extraction may not be required. For example, eye movement signals tend to be larger than other oral signals, so for eye movement applications it may not be necessary to extract other signals.

In some embodiments, key characteristics of the subcomponent signal, such as data points, thresholds, and / or data slope, can be extracted or isolated from the signal of interest. This may involve identifying brain-based signal patterns and converting them into commands to extract said function and / or issue commands to perform a task. A desired algorithm can be used to automatically estimate / calculate a value to represent the signals by a few relevant key values. There is a great variety of algorithms that can be implemented from simplistic methods such as adding, subtracting, multiplying, dividing, etc., to other complex techniques involving time-based or frequency-based approaches, principal component analysis, support vector machine , Genetic algorithm, sensitive and distinctive quantification of the learning vector, etc.

In some embodiments, the key characteristics of a subcomponent biosignal can be classified or converted into a command. The classification stage assigns a class to a set of characteristics extracted from the signals. The class may correspond to the type of mental states identified. This stage can also be referred to as "feature conversion".

Key feature information can be provided or displayed to a user / operator and // or can be used to perform tasks, such as comparing a sub-component extracted from bio-signals to a database of reference signals to control a device, assist in diagnosing a disease, disorder or condition, and / or reporting device function status.

After extraction of the subcomponent biosignal, the individual biosignals from the subcomponent can be displayed or used for other purposes, such as vital signs calculation, part of a command to control other device (s), or for additional processing, such as extracting particular features.

After separation into individual subcomponent signals, the raw brain signal can be further analyzed or separated into various frequency bands using 750 band pass filters and then displayed or used to issue a command. This may include 1-N 751 brain wave filters. Filters can be programmed or maintained in hardware for bands of interest. 751,753.

The 710 eye movement subcomponent signals can be displayed and observed for Rapid Eye Movement (REM) to determine the stage of sleep.

The heart signals 720 can display baseline heart rate and can be used to determine R wave peaks as well as heart rate variability.

Respiration signals 730 can be displayed to determine the respiratory rate.

Various data can be stored in a data storage device built into the internal oral unit and / or external unit.

In some embodiments of the system it may also include a tissue stimulation device.

In some applications, brain-based biosignal maps can be developed to allow topographic mapping of electrical activity for locations within the body.

An alternate embodiment incorporates multiple sensors such as oxygen saturation, head position via accelerometers, temperature, and brain-based signals shown in Figure 11.

Figure 12 shows an embodiment of the internal oral unit 800 including convex electrodes 60 configured to contact the center of the hard palate and concave electrodes 70 configured to contact the gums. The internal oral unit 800 may be an oral protection platform that may incorporate a biocompatible adhesive to maintain contact with the dentition and / or oral tissue similar to an adhesive for dentures. In some embodiments, the electronic circuitry 601 of the internal oral unit 800 performs some or all of the functions described above. The electrodes can be attached to the framework which provides a slight spring force against the gums and hard palate to ensure contact of the electrode with oral tissue.

Figure 13 shows oral connection device 900, incorporating a flexible transverse support band 901 to maintain contact with the hard palate and electrodes 11, 12, 13 and temperature sensor 52. Oral fixation device 900 may include a electronics 601 and a transmission unit 606.

Figure 14 shows an oral fixation device 1000 that includes a thin, flexible platform incorporating a biocompatible adhesive to maintain contact with the jaw and flexible electronic circuits. Electrodes 11, 12 and 13 are placed on the frame which provides a slight elastic force against the gums and hard palate to ensure contact of the electrode with the oral tissue. Some embodiments may include temperature 52, electronics 601, and drive unit 606.

Figure 15 shows a sagittal view of a human head with an example oral attachment mouthguard 800 with electrodes in contact with the left and right side of the hard palate, and one electrode in contact with the hard palate and the transmission system, and an exemplary embodiment of the external unit 650. The external unit may be a smartphone, a computer, or other computing device.

One embodiment of the present invention may utilize subcomponent signals of the multicomponent brain-based biosignals for obstructive sleep apnea ("OSA") screening, diagnosis and monitoring. OSA is a respiratory disorder caused by movement and blockage of the upper airway by the tongue and narrowing of the upper airway by the soft tissues within the nose, mouth, and throat that occurs during sleep. This phenomenon causes snoring and recurrent stoppage of breathing due to periodic obstruction of airflow in the upper airway during inhalation.

The current state of the art in the diagnosis of OSA and other sleep disorders involves the use of multichannel polysomnography to assess the EEG, respiratory signals, cardiac signals, muscle tone, eye movements, and leg movements of a sleeping patient. This requires the cumbersome connection of multiple EEG leads to the scalp, as well as other transducers such as microphones, electrocardiograph ('' ECG '') electrodes, electromyograph ("EMG") electrodes, and a pulse oximeter connected to various parts of a patient. Devices intended for home use may measure fewer available parameters, but still require multiple connections.

Embodiments of the invention allow the detection of brain-based multicomponent bio-signals (Fig. 2) from which subcomponent bio-signals, including brain-based electrical activity, including alpha waves or other waves can be extracted. (Fig. 3), eye movement (Fig. 5), respiration (FIG. 8) and ECG (FIG.

7). Signals from subcomponents of electrical brain activity can allow determination of sleep stage / state and overall sleep time. The bio-signals of respiratory subcomponents can allow the determination of apnea events. Biological signals from the eye movement subcomponent can allow determination of rapid eye movement (REM) sleep, and biological signals from the ECG subcomponent can allow heart rate determination during sleep. By analyzing individually or combinations of these subcomponent bio-signals, either manually or with a computer system / program, a patients sleep pattern can be determined to diagnose OSA.

In some embodiments, signal processing (including filtering, amplification, digitization, storage, etc.) and recording of some or all of the sub-component signals can be accomplished that can occur in computer chips embedded in a oral device that includes the sensor (s). The resulting data can be transmitted as it becomes available via wired or wireless technology (such as Bluetooth) to a receiving device (such as a smartphone, computer, or dedicated device) and / or uploaded to a receiving device at a later time. .

In other embodiments, the brain-based multicomponent bio-signal is transmitted to an external receiving device (such as a smartphone, a computer, or a dedicated device) for signal processing. The brain-based multicomponent biosignal can be transmitted as the sensor detects it, or can be recorded on a storage device in an oral device for retrieval at a later time.

If desired, in additional embodiments, the sensor that detects the brain-based multicomponent biosignal can be supplemented with additional secondary sensors (i.e., accelerometers, thermocouples, O ² saturation sensors, CO ² sensors, oxygen meters airflow, etc.) can be used in combination with the brain-based multi-component bio-signal to determine head position and oxygen desaturation and other events during sleep.

In some embodiments, the oral device can automatically turn off when it is removed from the patient's mouth. In other embodiments, the oral device can be turned off manually. The signals stored in the device can then be uploaded to a computer system that includes a software program for the interpretation of the signal data, and made available for diagnosis by a physician or other medical personnel.

In some embodiments, the electrical brain activity subcomponent signals extracted from the detected brain-based multicomponent signal can be used in conjunction with accelerometer signals to detect traumatic brain injuries in military personnel, sports participants, or others with risky professions or activities. such as concussions, strokes, and seizures. The detection of traumatic brain injuries can be facilitated by comparing the current subcomponent signals with the pre-existing reference signals. Pre-existing baseline signals can be recorded from a patient specific that is being analyzed or from a generic baseline derived from the consolidation of multiple previously recorded signals from the patient or a segment of the population. In other embodiments, these signals can be used to monitor performance.

In some embodiments, the subcomponent signals extracted from the detected brain-based multicomponent signal can be used to optimize training and provide performance feedback for athletes and soldiers to improve their capabilities during competition or in the field. Subcomponent signals extracted from the detected brain-based multicomponent signal can also be used in biofeedback applications.

In another embodiment, brain waves and muscle activity subcomponent signals extracted from the detected brain-based multicomponent signal can be used to determine the level of consciousness of a patient under general anesthesia.

In another embodiment, the subcomponent signals extracted from the detected brain-based multicomponent signal can be used to detect abnormal brain wave patterns indicative of hypoglycemia in people with diabetes.

In another embodiment, the brain-based biological signals, eye movement, head position, and respiration signals and other subcomponent signals extracted from the detected brain-based multicomponent signal can be used to help people. physically disabled but mentally capable of operating a wide variety of equipment and tools using a brain-computer interface that interprets subcomponent signals to operate a variety of equipment actions. For example, moving a motorized wheelchair or operating an artificial limb.

In another embodiment, the brain waves and eye movement subcomponent signals extracted from the detected brain-based multicomponent signal may be monitored for advertising or media programming evaluation.

In another embodiment, a user can be trained to alter their brain waves in order to send a sub-component signal extracted from the detected brain-based multi-component signal to a central computer to automatically control their mobile phone, video game console, television set. , music system or DVD player; change the temperature setting in the room; control an alarm system; control kitchen appliances; or control the computer system of a car. For example, the subcomponent signal extracted from the detected brain-based multicomponent signal can be used to detect drowsiness or sedatives or drug-related damage in the operator of a motor vehicle by monitoring subcomponent signals related to breathing, eye movement and other useful parameters. The device for this application can be in the form of a nose clip, mouthpiece, or combinations thereof that collects and processes brain-based multi-component signals through an on-board computer that can subsequently activate alarm systems and provide Notifications or alarms when the driver falls asleep or falls asleep at the wheel.

In another embodiment, a device can use a subcomponent signal extracted from the detected brain-based multicomponent signal, such as eye movement and other biological signals to control machines such as cars or airplanes using mind control especially for complex, fast or complex maneuvers. of emergency. For example, an application can improve reaction times for fighter or drone pilots and assist in aircraft control during high-performance situations or in times of war.

In an alternative embodiment of the system, the sensor and / or other elements of the system can be implanted in soft tissues, such as the soft palate or gums; or alternatively within the teeth or dental implants; or in a third alternative, in parts of the body other than the oral cavity. For example, the sensor and / or other elements of the system can be implanted in the soft palate and self-powered by piezoelectric material within the device. Or in another example, the sensor and / or other elements of the system can be implanted under the skin and periodically charged inductively, capacitively, optically or by other charging methods.

In another alternative embodiment, the sensor and / or other elements of the system may be located on the mouthpiece of a swimmer or underwater diver.

In a third alternative embodiment, the sensor and / or other elements of the system may be mounted on a nose clip designed for comfortable placement within an individual's nostrils.

Although many of the embodiments described herein describe applications in the oral cavity, the systems and methods described herein may also be applied to other internal tissues accessed through holes or incisions in the body.

In the above specification, various preferred embodiments have been described by way of example with reference to the accompanying drawings. However, it will be apparent that various modifications and changes may be made thereto, and additional exemplary embodiments may be implemented without departing from the broader scope. of the invention as set forth in the claims that follow. Accordingly, the specification and the drawings are to be considered in an illustrative rather than a restrictive sense.

Claims (9)

1. A system for detecting a brain-based multi-component electromagnetic biosignal (100) comprising: at least two biosignal sensors (11, 12, 13, 52, 60, 70) that are part of an oral fixation device (800, 900, 1000) configured to be placed in an oral cavity to bring the at least two bio-signal sensors with a buccal tissue (10), wherein said at least two bio-signal sensors have a wired or wireless connection with at least one external computer processing unit (650), the at least two bio-signal sensors being configured to detect a bio-signal raw multicomponent oral electromagnetic signal in the oral tissue and send the raw multicomponent oral electromagnetic biosignal to the at least one external computer processing unit (650) that includes a separate component analysis processor (700) that isolates at least a primary raw multicomponent electromagnetic bio-signal based on the brain (100) of a plurality of other subcomponents (710, 720, 730) within the raw multicomponent oral electromagnetic bio-signal, and wherein said computer processing unit External (650) further includes a plurality of brain wave bandpass filters (750) that are configured to further separate the multi-signal Brain-based raw component (100) in a plurality of frequency brain wave bands (751,752). The system of claim 1, further comprising a temperature sensor (52) connected to the oral fixation device (800, 900, 1000). The system of any of claims 1 or 2, wherein the oral fixation device (800, 900, 1000) is configured to engage teeth. The system of any of claims 1-3, wherein the oral fixation device (800, 900, 1000) includes concave electrodes (70) configured to contact the at least two biosignal sensors with the gums. The system of any of claims 1-3, wherein the oral fixation device (800, 900, 1000) includes convex electrodes (60) configured to contact the at least two bio-signal sensors with the hard palate tissue. The system of any of claims 1-5, wherein the at least one brain-based multicomponent biosignal (100) is correlated with the electroencephalogram (EEG) signal data. The system of any one of claims 1 to 6, wherein the at least one computer processing unit (650) is configured to generate a command based on filtered waveband data (751,752) from at least a raw, primary brain-based multicomponent electromagnetic biosignal (100). The system of any of claims 1-7, wherein the at least one computer processing unit (650) is configured to generate a diagnosis of a condition in response to comparing data from the waveband ( 751,752) filtered from the at least one primary raw brain-based multicomponent electromagnetic biosignal (100) with predetermined reference data for diagnosis of the condition. The system of claim 8, wherein the condition is at least one of obstructive sleep apnea, concussion, or seizure.